Gaussian Processes for Classification With Python
The Gaussian Processes Classifier is a classification machine learning algorithm.
Gaussian Processes are a generalization of the Gaussian probability distribution and can be used as the basis for sophisticated non-parametric machine learning algorithms for classification and regression.
They are a type of kernel model, like SVMs, and unlike SVMs, they are capable of predicting highly calibrated class membership probabilities, although the choice and configuration of the kernel used at the heart of the method can be challenging.
In this tutorial, you will discover the Gaussian Processes Classifier classification machine learning algorithm.
After completing this tutorial, you will know:
- The Gaussian Processes Classifier is a non-parametric algorithm that can be applied to binary classification tasks.
- How to fit, evaluate, and make predictions with the Gaussian Processes Classifier model with Scikit-Learn.
- How to tune the hyperparameters of the Gaussian Processes Classifier algorithm on a given dataset.
Let’s get started.
Gaussian Processes for Classification With Python
Photo by Mark Kao, some rights reserved.
Tutorial Overview
This tutorial is divided into three parts; they